Machine Learning Results
PCA 2 x PCA 1
ggplotly(empty_list[[1]]) %>% layout(legend = list(orientation = "h", x = 0.2, y = -0.2))
PCA 4 x PCA 1
ggplotly(empty_list[[3]]) %>% layout(legend = list(orientation = "h", x = 0.2, y = -0.2))
PCA 8 x PCA 2
ggplotly(empty_list[[13]]) %>% layout(legend = list(orientation = "h", x = 0.2, y = -0.2))
Multinomial Logistical Regression: Density Plot
ggplotly(ggplot(data2, aes(logit, fill=outcome))+geom_density(alpha=.3,) +
geom_vline(xintercept=0,lty=2))
PAM Clustering
PAM Cluster: 2 x 2
ggplotly(ggpairs(pamclust5,columns=1:2 ,aes(color=as.factor(rodPumpScale$Failure_Type))))
3 x 3
ggplotly(ggpairs(pamclust5,columns=1:3 ,aes(color=as.factor(rodPumpScale$Failure_Type))))
4 x 4
ggplotly(ggpairs(pamclust5,columns=1:4 ,aes(color=as.factor(rodPumpScale$Failure_Type))))
5 x 5
ggplotly(ggpairs(pamclust5,columns=1:5 ,aes(color=as.factor(rodPumpScale$Failure_Type))))